Multisatellite-Based Feeding Habitat Suitability Modeling of Albacore Tuna in the Southern Atlantic Ocean

Vayghan, Ali Haghi; Lee, Ming-An; Weng, Jinn-Shing; Mondal, Sandipan; Ching-Te, Lin; Wang, Yi-Chen

doi:10.3390/rs12162515

Cited by 15 publications

(6 citation statements)

References 71 publications

(153 reference statements)

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“…However, the current study had important limitations, such as the SST frontal area, which is associated with the habitat of large pelagic species, and we only use the GLM and GAM models to analyze the spatiotemporal distribution pattern of S. dumerili. Therefore, future research should consider the SST frontal data, and also include other habitat models such as the Geometric Mean Model [7], Arithmetic Mean Model [10], and Maximum Entropy Model [30,76], which one can apply and compare which is better for S. dumerili habitats.…”

Section: Environmental Factors Affecting the Greater Amberjackmentioning

confidence: 99%

“…This knowledge can support the management of ecosystems [1], prey abundance and dispersion [2], fish stocks and abundance [3], habitat conservation [4], and fisheries management [5]. Spatial and temporal distribution patterns can be used to identify and predict the fish habitats [6,7] and distribution based on satellite-derived environmental variables [8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

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Variability in the Spatiotemporal Distribution Patterns of Greater Amberjack in Response to Environmental Factors in the Taiwan Strait Using Remote Sensing Data

et al. 2022

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The environmental characteristics of the Taiwan Strait (TS) have been linked to variations in the abundance and distribution of greater amberjack (Seriola dumerili) populations. Greater amberjack is a commercially and ecologically valuable species in ecosystems, and its spatial distribution patterns are pivotal to fisheries management and conservation. However, the relationship between the catch rates of S. dumerili and the environmental changes and their impact on fish communities remains undetermined in the TS. The goal of this study was to determine the spatiotemporal distribution pattern of S. dumerili with environmental characteristics in the TS from south to north (20°N–29°N and 115°E–127°E), applying generalized additive models (GAMs) and spatiotemporal fisheries data from logbooks and voyage data recorders from Taiwanese fishing vessels (2014–2017) as well as satellite-derived remote sensing environmental data. We used the generalized linear model (GLM) and GAM to analyze the effect of environmental factors and catch rates. The predictive performance of the two statistical models was quantitatively assessed by using the root mean square difference. Results reveal that the GAM outperforms the GLM model in terms of the functional relationship of the GAM for generating a reliable predictive tool. The model selection process was based on the significance of model terms, increase in deviance explained, decrease in residual factor, and reduction in Akaike’s information criterion. We then developed a species distribution model based on the best GAMs. The deviance explained indicated that sea surface temperature, linked to high catch rates, was the key factor influencing S. dumerili distributions, whereas mixed layer depth was the least relevant factor. The model predicted a relatively high S. dumerili catch rate in the northwestern region of the TS in summer, with the area extending to the East China Sea. The target species is strongly influenced by biophysical environmental conditions, and potential fishing areas are located throughout the waters of the TS. The findings of this study showed how S. dumerili populations respond to environmental variables and predict species distributions. Data on the habitat preferences and distribution patterns of S. dumerili are essential for understanding the environmental conditions of the TS, which can inform future priorities for conservation planning and management.

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Section: Environmental Factors Affecting the Greater Amberjackmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Variability in the Spatiotemporal Distribution Patterns of Greater Amberjack in Response to Environmental Factors in the Taiwan Strait Using Remote Sensing Data

et al. 2022

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“…Two very common empirical HSI models-the arithmetic mean model (AMM) [43][44][45] and the geometric mean model (GMM) [43,44,46]-were employed to evaluate habitat preferences [32,33,36]. The SI values of each environmental factor were introduced into these two models [36][37][38].…”

Section: Grey Mullet Habitat Distributionmentioning

confidence: 99%

Distribution Patterns of Grey Mullet in the Taiwan Strait in Relation to Oceanographic Conditions

Teng,

Mondal,

et al. 2024

JMSE

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The depletion of the grey mullet population in the Taiwan Strait has generated interest in conservation initiatives and to enhance the effectiveness of restoration initiatives, it is essential to comprehend the specific habitat preferences of the species in question, particularly in relation to environmental changes. In this study, habitat suitability modelling was used to investigate the distribution patterns of grey mullet. Sea surface height (SSH) was the most significant predictor of the grey mullet standardized catch per unit effort (S.CPUE). Furthermore, sea surface temperature (SST) was the second most important oceanographic parameter, followed by mixed-layer depth (MLD). The grey mullet S.CPUE peaked in regions with the summed SST, Chla, salinity, SSH, MLD and current values being 20 °C, 0.9 mg/m3, 34.5 psu, 0.45 m, 18–38 m, and 0.25 m2/s2, respectively. From late October, HSI levels in the northern Taiwan ocean increased, with a consistent upward trend. The distribution expanded in November and December, focusing on the northern and mid-ocean regions west of Taiwan. HSI levels peak in January and February, especially in Taiwan’s coastal region. High HSI distribution shifts away from coastlines and diminishes in April. The findings of this study will contribute to the understanding of this specific species and the approach used in this study may be applicable to other fisheries stocks also.

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“…They are also used to develop biological surveys, evaluate reserve and management priorities, and foresee potential change under several scenarios for management or climate change [42]. Several researchers used the HSI to develop a model of potential fishing zones, as stated in studies [6], [25], [43]- [45]. Boitt and Aete [6] make use of surface temperature and chlorophyll-a remote sensing data by using a suitability index.…”

Section: Habitat Suitability Indexmentioning

confidence: 99%

A review on features and methods of potential fishing zone

Ya’acob,

Nik Dzulkefli,

Abdul Aziz

et al. 2024

IJECE

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This review focuses on the importance of identifying potential fishing zones in seawater for sustainable fishing practices. It explores features like sea surface temperature (SST) and sea surface height (SSH), along with classification methods such as classifiers. The features like SST, SSH, and different classifiers used to classify the data, have been figured out in this review study. This study underscores the importance of examining potential fishing zones using advanced analytical techniques. It thoroughly explores the methodologies employed by researchers, covering both past and current approaches. The examination centers on data characteristics and the application of classification algorithms for classification of potential fishing zones. Furthermore, the prediction of potential fishing zones relies significantly on the effectiveness of classification algorithms. Previous research has assessed the performance of models like support vector machines, naïve Bayes, and artificial neural networks (ANN). In the previous result, the results of support vector machine (SVM) were 97.6% more accurate than naive Bayes's 94.2% to classify test data for fisheries classification. By considering the recent works in this area, several recommendations for future works are presented to further improve the performance of the potential fishing zone models, which is important to the fisheries community.

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Multisatellite-Based Feeding Habitat Suitability Modeling of Albacore Tuna in the Southern Atlantic Ocean

Cited by 15 publications

References 71 publications

Variability in the Spatiotemporal Distribution Patterns of Greater Amberjack in Response to Environmental Factors in the Taiwan Strait Using Remote Sensing Data

Variability in the Spatiotemporal Distribution Patterns of Greater Amberjack in Response to Environmental Factors in the Taiwan Strait Using Remote Sensing Data

Distribution Patterns of Grey Mullet in the Taiwan Strait in Relation to Oceanographic Conditions

A review on features and methods of potential fishing zone

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